Current methods for estimating the timeliness of cancer diagnosis are not robust because dates of key defining milestones, for example first presentation, are uncertain. This is exacerbated when patients have other conditions (multimorbidity), particularly those that share symptoms with cancer. Methods independent of this uncertainty are needed for accurate estimates of the timeliness of cancer diagnosis, and to understand how multimorbidity impacts the diagnostic process.
Participants were diagnosed with oesophagogastric cancer between 2010 and 2019. Controls were matched on year of birth, sex, general practice and multimorbidity burden calculated using the Cambridge Multimorbidity Score. Primary care data (Clinical Practice Research Datalink) was used to explore population-level consultation rates for up to two years before diagnosis across different multimorbidity burdens. Five approaches were compared on the timing of the consultation frequency increase, the inflection point for different multimorbidity burdens, different aggregated time-periods and sample sizes.
We included 15,410 participants, of which 13,328 (86.5 %) had a measurable multimorbidity burden. Our new maximum likelihood estimation method found evidence that the inflection point in consultation frequency varied with multimorbidity burden, from 154 days (95 %CI 131.8–176.2) before diagnosis for patients with no multimorbidity, to 126 days (108.5–143.5) for patients with the greatest multimorbidity burden. Inflection points identified using alternative methods were closer to diagnosis for up to three burden groups. Sample size reduction and changing the aggregation period resulted in inflection points closer to diagnosis, with the smallest change for the maximum likelihood method.
Existing methods to identify changes in consultation rates can introduce substantial bias which depends on sample size and aggregation period. The direct maximum likelihood method was less prone to this bias than other methods and offers a robust, population-level alternative for estimating the timeliness of cancer diagnosis.
- • Cancer diagnostic timeliness estimates with low bias and uncertainty are needed.
- • Methods estimating change in consultation rate before diagnosis were explored.
- • The population-level maximum likelihood method had minimal bias.
- • The inflection point in consultation frequency varied with multimorbidity burden.
Early cancer diagnosis remains a focus of UK policy and research, with time to diagnosis a key outcome . Standardised definitions of time points and intervals describe patients’ pre-diagnostic pathways, some of which are objective (e.g. diagnosis date) while others are subjective, such as date of first presentation . Symptoms of possible cancer commonly have other causes , particularly in people with two or more chronic conditions (i.e. multimorbidity) . Multimorbidity also more than doubles the primary-care consultation rate , increasing the chance that possible cancer symptoms are recorded.
Modelling population-level consultation rates, which rise before a cancer diagnosis, may offer a robust alternative to patient-level metrics. Existing methods identify statistically significant deviations in consultation rate, either between groups or from historical trends . Statistical significance depends on effect and sample size; therefore, the time of consultation-rate change may vary with group size or underlying consultation rate. Furthermore, these methods cannot quantify the uncertainty around the timing of the deviation.
We urgently need workable and accurate metrics of the timeliness of cancer diagnosis that are independent of such biases and robust in patients with multimorbidity, who represent over three-quarters of patients aged ≥ 75 years – the peak age of cancer incidence . We explore using population-level consultation rates in primary care before cancer diagnosis as a measure of diagnostic timeliness across groups of patients with different multimorbidity burden. For two new methods and three approaches used previously we:
- 1. Identify the time before cancer diagnosis that primary-care consulting frequency increases above the norm (i.e. the inflection point).
- 2. Compare the inflection point between patients with different multimorbidity burden.
- 3. Investigate potential biases introduced by varying the period over which consultations are aggregated (28 days vs. 7, 14, and 21 days) and the sample size (100 % vs 50 %, 20 %, 10 %, and 5 %).
We illustrate the methods using oesophagastric cancer, which presents with a broad range of non-specific symptoms that commonly feature in chronic conditions .